Reign Analysis of Danish Monarchs

The task here is to load your Danish Monarchs csv into R using the tidyverse toolkit, calculate and explore the kings’ duration of reign with pipes %>% in dplyr and plot it over time.

Load the kings

Make sure to first create an .Rproj workspace with a data/ folder where you place either your own dataset or the provided kings.csv dataset.

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

read_csv2("data/danishmonarchs_group14.csv")

## ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
## Rows: 56 Columns: 8── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr (2): regenter, slaegt_navn
## dbl (6): reg_start, reg_slut, fodsel, dod, Reg_tid, leve_tid
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## # A tibble: 56 × 8
##    regenter         reg_start reg_slut fodsel   dod Reg_tid leve_tid slaegt_navn
##    <chr>                <dbl>    <dbl>  <dbl> <dbl>   <dbl>    <dbl> <chr>      
##  1 Gorm_1_den_gamle       936      958    908   964      22       56 Jelling    
##  2 Harald_1_blaata…       958      987    932   985      29       53 Jelling    
##  3 Svend_1_Tveskaeg       987     1014    963  1014      27       51 Jelling    
##  4 Harald_2              1014     1018    996  1018       4       22 Jelling    
##  5 Knud_1_den_store      1018     1035    995  1035      17       40 Jelling    
##  6 Hardeknud             1035     1042   1018  1042       7       24 Jelling    
##  7 Magnus_den_gode       1042     1047   1024  1047       5       23 Norske     
##  8 Svend_2_Estrids…      1047     1074   1019  1076      27       57 Jelling    
##  9 Harald_3_hen          1074     1080   1040  1080       6       40 Jelling    
## 10 Knud_2_den_hell…      1080     1096   1042  1086      16       44 Jelling    
## # ℹ 46 more rows

Look at the dataset that are you loading and check what its columns are separated by? (hint: open it in plain text editor to see)

List what is the

separator:semicolon

Create a kings object in R with the different functions below and inspect the different outputs.

read.csv()
read_csv()
read.csv2()
read_csv2()

# Vi fylder koden ind, for at finde ud af hvordan vi finder det brugbare data
library(tidyverse)

kings1 <- read.csv("data/danishmonarchs_group14.csv")
head(kings1)

##   regenter.reg_start.reg_slut.fodsel.dod.Reg_tid.leve_tid.slaegt_navn
## 1                      Gorm_1_den_gamle;936;958;908;964;22;56;Jelling
## 2                     Harald_1_blaatand;958;987;932;985;29;53;Jelling
## 3                    Svend_1_Tveskaeg;987;1014;963;1014;27;51;Jelling
## 4                            Harald_2;1014;1018;996;1018;4;22;Jelling
## 5                   Knud_1_den_store;1018;1035;995;1035;17;40;Jelling
## 6                          Hardeknud;1035;1042;1018;1042;7;24;Jelling

glimpse(kings1)

## Rows: 56
## Columns: 1
## $ regenter.reg_start.reg_slut.fodsel.dod.Reg_tid.leve_tid.slaegt_navn <chr> "G…

class(kings1)

## [1] "data.frame"

kings2 <- read_csv("data/danishmonarchs_group14.csv")

## Rows: 56 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): regenter;reg_start;reg_slut;fodsel;dod;Reg_tid;leve_tid;slaegt_navn
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(kings2)

## # A tibble: 6 × 1
##   `regenter;reg_start;reg_slut;fodsel;dod;Reg_tid;leve_tid;slaegt_navn`
##   <chr>                                                                
## 1 Gorm_1_den_gamle;936;958;908;964;22;56;Jelling                       
## 2 Harald_1_blaatand;958;987;932;985;29;53;Jelling                      
## 3 Svend_1_Tveskaeg;987;1014;963;1014;27;51;Jelling                     
## 4 Harald_2;1014;1018;996;1018;4;22;Jelling                             
## 5 Knud_1_den_store;1018;1035;995;1035;17;40;Jelling                    
## 6 Hardeknud;1035;1042;1018;1042;7;24;Jelling

glimpse(kings2)

## Rows: 56
## Columns: 1
## $ `regenter;reg_start;reg_slut;fodsel;dod;Reg_tid;leve_tid;slaegt_navn` <chr> …

class(kings2)

## [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

kings3 <- read.csv2("data/danishmonarchs_group14.csv")
head(kings3)

##            regenter reg_start reg_slut fodsel  dod Reg_tid leve_tid slaegt_navn
## 1  Gorm_1_den_gamle       936      958    908  964      22       56     Jelling
## 2 Harald_1_blaatand       958      987    932  985      29       53     Jelling
## 3  Svend_1_Tveskaeg       987     1014    963 1014      27       51     Jelling
## 4          Harald_2      1014     1018    996 1018       4       22     Jelling
## 5  Knud_1_den_store      1018     1035    995 1035      17       40     Jelling
## 6         Hardeknud      1035     1042   1018 1042       7       24     Jelling

glimpse(kings3)

## Rows: 56
## Columns: 8
## $ regenter    <chr> "Gorm_1_den_gamle", "Harald_1_blaatand", "Svend_1_Tveskaeg…
## $ reg_start   <int> 936, 958, 987, 1014, 1018, 1035, 1042, 1047, 1074, 1080, 1…
## $ reg_slut    <int> 958, 987, 1014, 1018, 1035, 1042, 1047, 1074, 1080, 1096, …
## $ fodsel      <int> 908, 932, 963, 996, 995, 1018, 1024, 1019, 1040, 1042, 105…
## $ dod         <int> 964, 985, 1014, 1018, 1035, 1042, 1047, 1076, 1080, 1086, …
## $ Reg_tid     <int> 22, 29, 27, 4, 17, 7, 5, 27, 6, 16, 9, 8, 30, 3, 9, 11, 25…
## $ leve_tid    <int> 56, 53, 51, 22, 40, 24, 23, 57, 40, 44, 45, 48, 69, 47, 46…
## $ slaegt_navn <chr> "Jelling", "Jelling", "Jelling", "Jelling", "Jelling", "Je…

class(kings3)

## [1] "data.frame"

kings4 <- read_csv2("data/danishmonarchs_group14.csv")

## ℹ Using "','" as decimal and "'.'" as grouping mark. Use `read_delim()` for more control.
## Rows: 56 Columns: 8── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr (2): regenter, slaegt_navn
## dbl (6): reg_start, reg_slut, fodsel, dod, Reg_tid, leve_tid
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(kings4)

## # A tibble: 6 × 8
##   regenter          reg_start reg_slut fodsel   dod Reg_tid leve_tid slaegt_navn
##   <chr>                 <dbl>    <dbl>  <dbl> <dbl>   <dbl>    <dbl> <chr>      
## 1 Gorm_1_den_gamle        936      958    908   964      22       56 Jelling    
## 2 Harald_1_blaatand       958      987    932   985      29       53 Jelling    
## 3 Svend_1_Tveskaeg        987     1014    963  1014      27       51 Jelling    
## 4 Harald_2               1014     1018    996  1018       4       22 Jelling    
## 5 Knud_1_den_store       1018     1035    995  1035      17       40 Jelling    
## 6 Hardeknud              1035     1042   1018  1042       7       24 Jelling

glimpse(kings4)

## Rows: 56
## Columns: 8
## $ regenter    <chr> "Gorm_1_den_gamle", "Harald_1_blaatand", "Svend_1_Tveskaeg…
## $ reg_start   <dbl> 936, 958, 987, 1014, 1018, 1035, 1042, 1047, 1074, 1080, 1…
## $ reg_slut    <dbl> 958, 987, 1014, 1018, 1035, 1042, 1047, 1074, 1080, 1096, …
## $ fodsel      <dbl> 908, 932, 963, 996, 995, 1018, 1024, 1019, 1040, 1042, 105…
## $ dod         <dbl> 964, 985, 1014, 1018, 1035, 1042, 1047, 1076, 1080, 1086, …
## $ Reg_tid     <dbl> 22, 29, 27, 4, 17, 7, 5, 27, 6, 16, 9, 8, 30, 3, 9, 11, 25…
## $ leve_tid    <dbl> 56, 53, 51, 22, 40, 24, 23, 57, 40, 44, 45, 48, 69, 47, 46…
## $ slaegt_navn <chr> "Jelling", "Jelling", "Jelling", "Jelling", "Jelling", "Je…

class(kings4)

## [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

Answer: 1. Which of these functions is a tidyverse function? Read data with it below into a kings object kings3 og kings4 er de korrekte, da den afspejler dataene i vores datasæt, 56 observationer og 8 collums

What is the result of running class() on the kings object created with a tidyverse function. når man kører funktionen class() på kings er resultat= “spec_tbl_df” “tbl_df” “tbl” “data.frame”
How many columns does the object have when created with these different functions? Den har 8 variabler, eller 8 objekter
Show the dataset so that we can see how R interprets each column

kings <- kings4

class(kings)

## [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

ncol(kings)

## [1] 8

glimpse(kings)

## Rows: 56
## Columns: 8
## $ regenter    <chr> "Gorm_1_den_gamle", "Harald_1_blaatand", "Svend_1_Tveskaeg…
## $ reg_start   <dbl> 936, 958, 987, 1014, 1018, 1035, 1042, 1047, 1074, 1080, 1…
## $ reg_slut    <dbl> 958, 987, 1014, 1018, 1035, 1042, 1047, 1074, 1080, 1096, …
## $ fodsel      <dbl> 908, 932, 963, 996, 995, 1018, 1024, 1019, 1040, 1042, 105…
## $ dod         <dbl> 964, 985, 1014, 1018, 1035, 1042, 1047, 1076, 1080, 1086, …
## $ Reg_tid     <dbl> 22, 29, 27, 4, 17, 7, 5, 27, 6, 16, 9, 8, 30, 3, 9, 11, 25…
## $ leve_tid    <dbl> 56, 53, 51, 22, 40, 24, 23, 57, 40, 44, 45, 48, 69, 47, 46…
## $ slaegt_navn <chr> "Jelling", "Jelling", "Jelling", "Jelling", "Jelling", "Je…

tail(kings)

## # A tibble: 6 × 8
##   regenter      reg_start reg_slut fodsel   dod Reg_tid leve_tid slaegt_navn
##   <chr>             <dbl>    <dbl>  <dbl> <dbl>   <dbl>    <dbl> <chr>      
## 1 Christian_9        1864     1906   1818  1906      42       88 Glucksburg 
## 2 Frederik_8         1906     1912   1843  1912       6       69 Glucksburg 
## 3 Christian _10      1912     1947   1870  1947      35       77 Glucksburg 
## 4 Frederik_9         1947     1972   1899  1972      25       73 Glucksburg 
## 5 Margrete_2         1972     2024   1940    NA      52       NA Glucksburg 
## 6 Frederik_10        2024     2025   1968    NA       1       NA Glucksburg

Calculate the duration of reign for all the kings in your table

You can calculate the duration of reign in years with mutate function by subtracting the equivalents of your startReign from endReign columns and writing the result to a new column called duration. But first you need to check a few things:

Is your data messy? Fix it before re-importing to R
Do your start and end of reign columns contain NAs? Choose the right strategy to deal with them: na.omit(), na.rm=TRUE, !is.na()

Create a new column called duration in the kings dataset, utilizing the mutate() function from tidyverse. Check with your group to brainstorm the options.

# YOUR CODE
kings %>% 
  mutate(duration= reg_slut - reg_start) %>%
  select(duration)

## # A tibble: 56 × 1
##    duration
##       <dbl>
##  1       22
##  2       29
##  3       27
##  4        4
##  5       17
##  6        7
##  7        5
##  8       27
##  9        6
## 10       16
## # ℹ 46 more rows

Calculate the average duration of reign for all rulers

Do you remember how to calculate an average on a vector object? If not, review the last two lessons and remember that a column is basically a vector. So you need to subset your kings dataset to the duration column. If you subset it as a vector you can calculate average on it with mean() base-R function. If you subset it as a tibble, you can calculate average on it with summarize() tidyverse function. Try both ways!

You first need to know how to select the relevant duration column. What are your options?
Is your selected duration column a tibble or a vector? The mean() function can only be run on a vector. The summarize() function works on a tibble.
Are you getting an error that there are characters in your column? Coerce your data to numbers with as.numeric().
Remember to handle NAs: mean(X, na.rm=TRUE)

# YOUR CODE
kings %>% 
  mutate(duration = reg_slut - reg_start) %>% 
  summarise(mean_duration = mean(duration, na.rm=TRUE))

## # A tibble: 1 × 1
##   mean_duration
##           <dbl>
## 1          19.6

How many and which kings enjoyed a longer-than-average duration of reign?

You have calculated the average duration above. Use it now to filter() the duration column in kings dataset. Display the result and also count the resulting rows with count()

# YOUR CODE
kings %>% 
  mutate(duration = reg_slut - reg_start) %>% 
  filter(duration > 19.5537) %>% 
  count()

## # A tibble: 1 × 1
##       n
##   <int>
## 1    27

How many days did the three longest-ruling monarchs rule?

Sort kings by reign duration in the descending order. Select the three longest-ruling monarchs with the slice() function
Use mutate() to create Days column where you calculate the total number of days they ruled
Here we willconsider the transition year (with 366 days) in our calculation

# YOUR CODE
kings %>% 
  mutate(duration = reg_slut - reg_start) %>% 
  arrange(desc(duration)) %>% 
  slice(c(1, 2, 3)) %>% 
  mutate(regtid_dage = 365 * duration)

## # A tibble: 3 × 10
##   regenter reg_start reg_slut fodsel   dod Reg_tid leve_tid slaegt_navn duration
##   <chr>        <dbl>    <dbl>  <dbl> <dbl>   <dbl>    <dbl> <chr>          <dbl>
## 1 Christi…      1588     1648   1577  1648      60       71 Oldenborg         60
## 2 Margret…      1972     2024   1940    NA      52       NA Glucksburg        52
## 3 Erik_7_…      1396     1439   1382  1459      43       77 Jelling           43
## # ℹ 1 more variable: regtid_dage <dbl>

# Herunder kan man tjekke efter om det passer, og det gør det
# 60*365 = 21900
# 52*365 = 18980
# 43*365 = 15695

Challenge: Plot the kings’ duration of reign through time

What is the long-term trend in the duration of reign among Danish monarchs? How does it relate to the historical violence trends ?

Try to plot the duration of reign column in ggplot with geom_point() and geom_smooth()
In order to peg the duration (which is between 1-99) somewhere to the x axis with individual centuries, I recommend creating a new column midyear by adding to startYear the product of endYear minus the startYear divided by two (startYear + (endYear-startYear)/2).
Now you can plot the kings dataset, plotting midyear along the x axis and duration along y axis
BONUS: add a title, nice axis labels to the plot and make the theme B&W and font bigger to make it nice and legible!

# Vores visualisering af alle regenternes regeringstid, med en videreudvikling som gør vores ggplot interaktiv, ved hjælp af plotly
library(dplyr)
library(ggplot2)
library(plotly)

## Warning: pakke 'plotly' blev bygget under R version 4.4.3

## 
## Vedhæfter pakke: 'plotly'

## Det følgende objekt er maskeret fra 'package:ggplot2':
## 
##     last_plot

## Det følgende objekt er maskeret fra 'package:stats':
## 
##     filter

## Det følgende objekt er maskeret fra 'package:graphics':
## 
##     layout

kings_plotdata <- kings %>%
  mutate(
    duration = reg_slut - reg_start,
    midyear = reg_start + duration / 2,
    tooltip_text = paste0("Name: ", regenter, 
                          "<br>Midyear: ", midyear, 
                          "<br>Duration: ", duration, " years")
  )

p <- ggplot(kings_plotdata, aes(x = midyear, y = duration)) +
  geom_point(aes(text = tooltip_text), color = "#0072B2", size = 3, alpha = 0.8) +
  geom_text(aes(label = regenter), vjust = -1, size = 3, check_overlap = TRUE) +
  geom_smooth(method = "loess", se = TRUE, color = "#D55E00", fill = "#D55E00", alpha = 0.2) +
  labs(
    title = "Length of Reign Over Time",
    subtitle = "Danish monarchs shown by midpoint of reign",
    x = "Midpoint of Reign (Year)",
    y = "Duration of Reign (Years)",
    caption = "Source: kings dataset"
  ) +
  theme_bw()

## Warning in geom_point(aes(text = tooltip_text), color = "#0072B2", size = 3, :
## Ignoring unknown aesthetics: text

# med dette kan man gøre grafen interaktiv
ggplotly(p, tooltip = "text")

## `geom_smooth()` using formula = 'y ~ x'

And to submit this rmarkdown, knit it into html. But first, clean up the code chunks, adjust the date, rename the author and change the eval=FALSE flag to eval=TRUE so your script actually generates an output. Well done!